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	基于深度学习的全景室内三维重建
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	摘~~要
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{\heiti}\quad {\songti

室内场景三维重建可以为人们的生活提供高精度数字化的信息，从而为室内场景的智能服务提供帮助，因此具有重要意义。但室内场景的弱纹理环境与复杂
的空间结构使得传统三维重建算法难以取得较好的效果。

深度学习技术在计算机视觉领域的出色表现为这个问题的解决提供了新的思路。深度学习方法越来越多的用于三维重建领域。同时随着全景设备的
发展，通过消费级全景相机采集数据越来越方便，全景图相比于透视图可以为三维重建提供更多的视角，具有优势。本文希望结合以上两点，使用全景图
对场景进行拍摄，基于开源三维重建系统Colmap，引入深度学习特征点和深度学习匹配算法，从而提高在室内场景下的三维重建效果。

本文的主要贡献在于：

\begin{enumerate}
	\item 通过引入深度学习的特征点与匹配算法对三维重建进行优化;
 \item 使用全景图进行重建，使用更少的图片提供更丰富的视角;
 \item 采用模块化设计，实现了一个可拓展的三维重建工具箱。
\end{enumerate}

本文选择了ETH3D的测试集进行了重建效果观测，比较本文方法、传统方法与端到端深度学习方法的重建效果，选择ETH3D的训练集进行指标计算，比较了本文方法与传统方法的精度和完整度。
最后，本文通过自己采集的全景图数据进行真实场景的三维重建实验验证本文方法在真实场景的可行性。

通过实验论证，本文在室内场景相比于传统方法取得了更好的效果，可以实际应用到真实的室内全景图数据上。

%特点
%实验结果


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\noindent {\heiti\zihao{5}关键词:}\quad{
\songti
三维重建, 
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全景图, 
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深度学习, 
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室内环境}~\\



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	{\bf {Panorama indoor 3D reconstruction based on deep learning} }
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	\bf{	ABSTRACT}
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{\heiti}\quad {\songti
The 3D reconstruction of indoor scenes can provide high-precision digital information for people's life. Thus
it is of great importance to provide intelligent services for indoor scene because it can help to provide intelligent services for indoor scenes. 
However, the weak texture environment and complex spatial structure of indoor scenes make it difficult for traditional 3D reconstruction algorithms toachieve better results.

The outstanding performance of deep learning techniques in computer vision provides a new view to solve this problem. Deep learning methods
are increasingly used in the field of 3D reconstruction. Meanwhile, with the development of panoramic devices, it is increasingly convenient to collect data through consumer-grade panoramic cameras
convenient, panoramas can provide more perspectives which has advantages on 3D reconstruction compared with perspective cameras. In this paper, we hope to combine the above two points to make
a system based on the open-source 3D reconstruction system, Colmap. We introduce deep learning feature points and deep learning matching algorithm to improve the performance of indoor scenes.

The main contributions of this paper are:
\begin{enumerate}
	\item optimizing the 3D reconstruction by introducing deep learning feature points and matching algorithm;
 \item using panoramic images for reconstruction, which provides a richer perspective with fewer images;
 \item using a modular design, an expandable 3D reconstruction toolbox is implemented.
\end{enumerate}

In this paper, a test set of ETH3D was selected for finalization experiments to compare the reconstruction results of this paper, traditional methods and end-to-end deep learning methods.
We select the training set of ETH3D for quantitative experiments, and compare the accuracy and completeness of this method with the traditional method.
Finally, this paper verifies the feasibility of this method in real scenes by conducting 3D reconstruction experiments of real scenes with the panoramic image data collected by ourselves.
The feasibility of this paper in real scenes is verified.
Through the experimental demonstration, this paper achieves better results in indoor scenes compared with traditional methods, and can be practically applied to real indoor panorama data.
The method can be applied to real indoor panorama data.
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\noindent{\heiti\zihao{5}\bf {Key words:}}\quad
{\songti
3D reconstruction, Panorama, Deep Learning, Indoor Environment
}
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